ADALINE Robust Multistep Training Algorithm
The article considers the multi-step ADALINE training algorithm when using the correntropy information criterion as a learning criterion, determines the conditions for the convergence of the algorithm, and shows that in the steady state the resulting estimate is unbiased. The importance of choosing...
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| Datum: | 2020 |
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| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України
2020
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| Schriftenreihe: | Control systems & computers |
| Schlagworte: | |
| Online Zugang: | http://dspace.nbuv.gov.ua/handle/123456789/181183 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Zitieren: | ADALINE Robust Multistep Training Algorithm / O.G. Rudenko, O.O. Bezsonov // Control systems & computers. — 2020. — № 3. — С. 15-27. — Бібліогр.: 40 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| Zusammenfassung: | The article considers the multi-step ADALINE training algorithm when using the correntropy information criterion as a learning criterion, determines the conditions for the convergence of the algorithm, and shows that in the steady state the resulting estimate is unbiased. The importance of choosing the width of the Gaussian core, which affects the convergence rate of the estimation algorithms and the error in the steady state, is noted, and the feasibility of developing procedures for adaptive correction of the core width is indicated. |
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